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Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.

Francisco Garibaldi-Márquez^1,2, Gerardo Flores¹, Diego A Mercado-Ravell^3,4

¹Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, Leon 37150, Guanajuato, Mexico.

Sensors (Basel, Switzerland)

|April 23, 2022

Summary

Accurately identifying crops and weeds in fields is key for automated farming. A new Convolutional Neural Network (CNN) approach achieved 97% accuracy in classifying corn, narrow-leaf weeds, and broadleaf weeds in natural settings.

Keywords:

classic ML corn and weed classification deep learning multi-plant image multi-plant species natural environment

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Automated weed control is crucial for modern agriculture but faces challenges in natural field conditions.
Existing weed control methods are often limited to controlled environments, hindering real-world application.
Accurate crop and weed identification is a prerequisite for developing effective automated weed management systems.

Purpose of the Study:

To develop and evaluate a robust classification approach for distinguishing Zea mays (corn) from narrow-leaf weeds (NLW) and broadleaf weeds (BLW) using images captured in natural field conditions.
To create and utilize a comprehensive image dataset of corn and weeds at various growth stages and locations.
To compare the performance of a Convolutional Neural Network (CNN) based classification with a shallow learning approach for weed identification.

Main Methods:

A large dataset of multi-plant images was collected under diverse natural field conditions.
Regions of Interest (ROIs) were extracted using Connected Component Analysis (CCA).
ROIs were classified using a CNN model and compared against a shallow learning method, with performance evaluated using accuracy, precision, recall, and F1-score.

Main Results:

The CNN-based approach demonstrated superior performance in weed classification compared to the shallow learning method.
The CNN model achieved a high accuracy of 97% for classifying corn, NLW, and BLW.
The study confirmed the effectiveness of the CNN approach for weed classification in early growth stages within natural corn fields.

Conclusions:

Convolutional Neural Networks offer a highly effective solution for crop and weed discrimination in complex, natural agricultural environments.
The developed CNN-based system provides a promising foundation for advancing automated weed control technologies.
Accurate plant classification using machine learning is vital for the future of precision agriculture and sustainable farming practices.